Can low-cost, scalable, online intervention increase youth informed political participation in electoral authoritarian contexts?
Bibliographic record
Abstract
Young citizens vote at low rates. This contributes to political parties' tendency to de-prioritize the youth's preferences. We analyze the effects of low-cost, theoretically-grounded, online interventions designed to encourage young Moroccans to turn out and cast an informed vote in the 2021 elections. Those interventions aimed at (1) lowering participation costs by providing information about the registration process, (2) increasing the expected benefit of voting by highlighting both the election’s stakes, and (3) the distance between respondents’ policy preferences and parties' platforms. The interventions failed to increase turnout on average. Yet, the two treatments designed to increase expected benefits increased turnout intentions for those unsure about whether to vote. Moreover, information about parties' platforms increased support for the party that is closer to respondents’ preferences, leading to better-informed voting. Contributing to information processing theories, we find that party updating was subjected to motivated reasoning, even though parties are weakly institutionalized.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".